Mixed-effects models for modeling cardiac functions and treatment effects

MIXED-EFFECTS MODELS FOR MODELINGCARDIAC FUNCTIONS AND TREATMENT EFFECTS Hyejeong JangApril 15, 2011 Mixed-effects model is an efficient tool for analyzing longitudinal data. The randomeffects in mixed-effects model can be used to capture the correlations among repeatedmeasurements within a subject. The time points are not fixed and all available data can beused in mixed-effects model provided data are missing at random. For this reason, wefocus on applying mixed-effects models to the repeated measurements of cardiac functionincluding heart rate, left ventricle developed pressure, and coronary flow in theGlutathione-S-transferase P (GSTP) gene knockout and wild-type mice followingischemialreperfusion injury performed in the isolated, Langendorff-perfused heart.Cardiac function is measured during three time periods: pre-ischemia, ischemia (no flow),and reperfusion. We developed piecewise nonlinear mixed-effects model to describe thedifferent aspects of the cardiac function during each period. We applied nonlinear mixed-effects models and a changing point model to examine how cardiac function was alteredby ischemialreperfusion-induced injury and for comparison between mouse strains.These findings provide evidence of a new application for the mixed-effects model inphysiological and pharmacological studies of the heart.